Least squares variance component estimation for surveying network adjustment

The stochastic model of least squares adjustment plays an essential role in geodetic network data processing because the model describes the accuracy of the measurements and their correlation with each other. Knowledge of weights of the observables is necessary to provide a better understanding of t...

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Main Author:	Bidi, Nur Khalilah
Format:	Thesis
Language:	English
Published:	2019
Subjects:	TH434-437 Quantity surveying
Online Access:	http://eprints.utm.my/id/eprint/86218/1/NurKhalilahBidiMFABU2019.pdf
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spelling	my-utm-ep.862182020-08-30T09:07:57Z Least squares variance component estimation for surveying network adjustment 2019 Bidi, Nur Khalilah TH434-437 Quantity surveying The stochastic model of least squares adjustment plays an essential role in geodetic network data processing because the model describes the accuracy of the measurements and their correlation with each other. Knowledge of weights of the observables is necessary to provide a better understanding of the sources of errors and to model the error, hence the weights need to be determined correctly. For geodetic applications, it is crucial to have knowledge about covariance matrix of the observables since variance components are most commonly used to determine a realistic precision. This study focuses on the estimation of variance components from different types of data for geodetic applications, which include deformation survey and cadastral survey. Least Squares Variance Component Estimation (LS-VCE) method was used in this study because the method is simple, flexible and attractive due to the precision of variance estimators that can be directly obtained. For deformation monitoring network, simulations of geodetic and Kenyir dam networks data were performed. Meanwhile, for cadastral observation data from several types of instruments such as chain measurement, Electronic Distance Measurement and total station were utilized. The results revealed that the estimated variance components for distance scale error ap seem to become unrealistic for each data tested as the baseline of the networks was not long enough. In addition, it was found that the traverse network which included chain survey, showed insignificant result to the precision of station coordinates when the measurements were combined. The distance-dependent model was selected as the best model for Kenyir dam network since W-test values of Epoch 1 and 2 were 0.30 and 0.50, where the expectation and the variance of W-test values were 0 and 1, respectively. The findings showed that LS-VCE method was very reliable in various geodetic applications. In conclusion, the program developed is valuable for professional groups which include surveyors and engineers, as well as geophysics and geologists. 2019 Thesis http://eprints.utm.my/id/eprint/86218/ http://eprints.utm.my/id/eprint/86218/1/NurKhalilahBidiMFABU2019.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:131648 masters Universiti Teknologi Malaysia, Faculty of Built Environment & Surveying Faculty of Built Environment & Surveying
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TH434-437 Quantity surveying
spellingShingle	TH434-437 Quantity surveying Bidi, Nur Khalilah Least squares variance component estimation for surveying network adjustment
description	The stochastic model of least squares adjustment plays an essential role in geodetic network data processing because the model describes the accuracy of the measurements and their correlation with each other. Knowledge of weights of the observables is necessary to provide a better understanding of the sources of errors and to model the error, hence the weights need to be determined correctly. For geodetic applications, it is crucial to have knowledge about covariance matrix of the observables since variance components are most commonly used to determine a realistic precision. This study focuses on the estimation of variance components from different types of data for geodetic applications, which include deformation survey and cadastral survey. Least Squares Variance Component Estimation (LS-VCE) method was used in this study because the method is simple, flexible and attractive due to the precision of variance estimators that can be directly obtained. For deformation monitoring network, simulations of geodetic and Kenyir dam networks data were performed. Meanwhile, for cadastral observation data from several types of instruments such as chain measurement, Electronic Distance Measurement and total station were utilized. The results revealed that the estimated variance components for distance scale error ap seem to become unrealistic for each data tested as the baseline of the networks was not long enough. In addition, it was found that the traverse network which included chain survey, showed insignificant result to the precision of station coordinates when the measurements were combined. The distance-dependent model was selected as the best model for Kenyir dam network since W-test values of Epoch 1 and 2 were 0.30 and 0.50, where the expectation and the variance of W-test values were 0 and 1, respectively. The findings showed that LS-VCE method was very reliable in various geodetic applications. In conclusion, the program developed is valuable for professional groups which include surveyors and engineers, as well as geophysics and geologists.
format	Thesis
qualification_level	Master's degree
author	Bidi, Nur Khalilah
author_facet	Bidi, Nur Khalilah
author_sort	Bidi, Nur Khalilah
title	Least squares variance component estimation for surveying network adjustment
title_short	Least squares variance component estimation for surveying network adjustment
title_full	Least squares variance component estimation for surveying network adjustment
title_fullStr	Least squares variance component estimation for surveying network adjustment
title_full_unstemmed	Least squares variance component estimation for surveying network adjustment
title_sort	least squares variance component estimation for surveying network adjustment
granting_institution	Universiti Teknologi Malaysia, Faculty of Built Environment & Surveying
granting_department	Faculty of Built Environment & Surveying
publishDate	2019
url	http://eprints.utm.my/id/eprint/86218/1/NurKhalilahBidiMFABU2019.pdf
_version_	1747818507063525376

Least squares variance component estimation for surveying network adjustment

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